scholarly journals Voice Controlled Personal Assistive Bot with Object Detection

2021 ◽  
Vol 12 (4) ◽  
pp. 578-584
Author(s):  
S. A. Sivasankari, Et. al.
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Raspberry Pi ◽  
Machine Learning Algorithm ◽  
Primary Reason ◽  
Person Detection ◽  
Personal Assistance ◽  
Human Voice ◽  
The Face

Currently innovation has made our lives simpler for individuals. Be that as it may, from this innovation, certain gatherings of individuals need more assistance and backing for old or handicap individuals. This innovation can make a method of having a typical life. Thus, we zeroed in on the idea of an individual colleague robot. The fundamental objectisto supply helptodebilitated people.ThisPerson alassistive Bot help to decrease the manual endeavors being put by people in their everyday errands. The intention is to execute a specialized work that is voice controlling one which can act as a PA that can performvarious errands or administrations for a person.This is uncommonly intended for this group of people asits primary reason for existing is to supply help to relate senior or debilitated individual. The human voice order is given to the mechanical right hand distantly,by utilizing a voice order.The automaton will perform different movements: Forward, Backward, Right, Left and start/stop activities. The robot can likewise peruse and perceive the letter sets and text and the words which are said by the person will check from the google dictionary and printasatext.The capability of the robotisto detect the objects and relocate the m from one place to another and includes the face recognition. So, our main ideology is to create a personal assistance bot, which is capable of handling small objects. We are planning to make the bot consisting of four wheels and an arm placed on top. Using Raspberry Pi, we are communicating the sensors and motors throughour voice commands. Smart assistants like Google for android,Sir ifor Apple,Corton a for Microsoft,the seassistive gives us a platform to communicate to a bot. Asweare programming on Python, Amodule name Pyaudio will helpto communicate with a bot and having the extra feature like ‘Speechto Text’.And we would like to add an extra feature like object and person detection. A Camera module will be installed for capturing video and recognize the Humans and objects carried out with Machine Learning Algorithm

scholarly journals Real Time Face Recognition in Group Images using LBPH

2019 ◽  
Vol 8 (2) ◽  
pp. 1362-1367
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Testing Dataset ◽  
Negative Images ◽  
The Face ◽  
Haar Cascade

Face recognition is a beneficial work in computer vision based applications. The goal of the proposed system is to provide complete face recognitions system capable of working a group of images. The faces are detected and verified the identity of an individual using a machine learning algorithm. The haar cascade detects the face from a group of images for training and testing dataset. The dataset contained positive and negative images for training and testing. The LBPH algorithm recognizes the faces from input images. The proposed system detects and recognizes faces with 98% accuracy

scholarly journals Cloud Based Face Recognition using Machine Learning

2021 ◽  
pp. 1003-1006
Author(s):  
Pramod R ◽  
Rakshanda D. Bellary ◽  
Riya Bharti ◽  
Sushma S
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Raspberry Pi ◽  
Authentication Mechanism ◽  
Face Images ◽  
Memory Card ◽  
Main Motive ◽  
Entrance Door ◽  
Identity Card ◽  
The Face

The main motive of this paper is to implement a system where the employees and the visitors are granted access to enter the office by recognizing their face images. And henceforth, the access is granted only when the employee or the visitor enters the correct pin into the keypad which is concerned for authentication purpose.Without any usage of the tag keys or identity card an employee can easily unlock the entrance door once his face is recognized. A Raspberry pi, a camera, a memory card and a keypad is the hardware components that is required in this system. The face recognition and the authentication carried out by the keypad is controlled by the cloud based platforms and the local based Web Services. The authentication mechanism and the face recognition provides a safe and and increased level of security which gives a protection against spoofing attacks where there is no need of carrying any tag keys or access cards.

scholarly journals Multi-Model Biometric Authentication System for Smart Attendance

2020 ◽  
Vol 8 (6) ◽  
pp. 4284-4287
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Raspberry Pi ◽  
Machine Learning Algorithm ◽  
Biometric Authentication ◽  
Biometric System ◽  
Authentication System ◽  
Paper Method

To increase the success rate in academics, attendance is an essential aspect for every student in schools and degree colleges. In olden days, this attendance is manually taken by teachers with pen and paper method, which consumes more amount of time in their busy management scheduling era. To make this attendance taking more comfortable and more accurate, a multi model biometric system for attendance monitoring system is proposed using a Raspberry Pi single-board computer. The camera and biometric device which is connected to the system gathers Information regarding the students by recognizing their faces and their fingerprint simultaneously. If both of them match with the student details stored in the database, then the system will be sending an alert about the student presence in the class. The student details which is stored into the database is collected from the students initially. By using these details like images and fingerprints the system is trained by using a Convolutional Neural Network (CNN) Machine Learning Algorithm.

scholarly journals AI Enabled Face Mask Detector

2021 ◽  
Vol 9 (VI) ◽  
pp. 2980-2986
Author(s):  
Shivraj Patil
Keyword(s):  
Machine Learning ◽  
Global Health ◽  
Image Classification ◽  
Learning Algorithm ◽  
Face Mask ◽  
Classification Method ◽  
Machine Learning Algorithm ◽  
Health Crisis ◽  
The Face

Covid-19 pandemic is causing a global health crisis. To battle against the virus everyone should wear a face mask. The face mask detector in this study is developed with a machine learning algorithm called MobileNetV2 which is an image classification method. The steps to build the model are collecting the data, pre-processing, split the data, testing the model and implement the model.

scholarly journals Student Smart Attendance Through Face Recognition using Machine Learning Algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 2348-2352
Keyword(s):  
Face Recognition ◽  
Learning Algorithm ◽  
Working Hours ◽  
Machine Learning Algorithm ◽  
Decision Tree Algorithm ◽  
Cascade Algorithm ◽  
Face Region ◽  
The Face ◽  
Haar Cascade ◽  
Classroom Time

In today’s competitive world, with very less classroom time and increasing working hours, lecturers may need tools that can help them to manage precious class hours efficiently. Instead of focusing on teaching, lecturers are stuck with completing some formal duties, like taking attendance, maintaining the attendance record of each student, etc. Manual attendance marking unnecessarily consumes classroom time, whereas smart attendance through face recognition techniques helps in saving the classroom time of the lecturer. Attendance marking through face recognition can be implied in the classroom by capturing the image of the students in the classroom via the camera installed. Later through the HAAR Cascade algorithm and MTCNN model, face region needs to be taken as interest and the face of each student is bounded through a bounding box, and finally, attendance can be marked into the database based on their presence by using Decision Tree Algorithm.

scholarly journals A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition

2021 ◽  
Vol 11 (2) ◽  
pp. 115-120
Author(s):  
Muhammad Shakeel Faridi ◽  
◽  
Muhammad Azam Zia ◽  
Zahid Javed ◽  
Imran Mumtaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Comparative Analysis ◽  
Learning Algorithm ◽  
Research Work ◽  
Classification Systems ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Detection Systems

Feature extracting and training module can be done by using face recognition neural learning techniques. Moreover, these techniques are widely employed to extract features from human images. Some detection systems are capable to scan the full body, iris detection, and finger print detection systems. These systems have deployed for safety and security intension. In this research work, we compare different machine learning algorithms for face recognition. Four supervised face recognition machine-learning classifiers such as Principal Component Analysis (PCA), 1-nearest neighbor (1-NN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are considered. The efficiency of multiple classification systems is also demonstrated and tested in terms of their ability to identify a face correctly. Face Recognition is a technique to identify faces of people whose images are stored in some databases and available in the form of datasets. Extensive experiments conducted on these datasets. The comparative analysis clearly shows that which machine-learning algorithm is the best in terms of accuracy of image detection. Despite the fact, other identification methods are also very effective; face recognition has remained a major focus of research due to its non-meddling nature and being the easy method of personal identification for people. The findings of this work would be useful identification of a suitable machine-learning algorithm in order to achieve better face recognition accuracy.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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